The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided in this application is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
As data becomes more available and as the size of datasets increase, many analytical processes suffer from the “curse of dimensionality”. The phrase “curse of dimensionality,” which was coined by Richard E. Bellman (“Adaptive control processes: a guided tour;” 1961; Princeton University Press), refers to the problems that arise when analyzing and organizing data in hyper-dimensional spaces (e.g. datasets with hundreds, thousands, or millions of features or variables) that do not occur in low-dimensional settings.
All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
Although computer technology continues to advance, processing and analyzing hyper-dimensional datasets is computationally intensive. For example, with iterative modeling processes the computation time required to search all possible model component combinations increases exponentially with each addition of an additional model component. In particular, there is a need to reduce computational requirements in hyper-dimensional spaces in a way that makes techniques such as iterative modeling processes more appropriate for solving complex problems using large datasets. One way to reduce computational requirements in iterative modeling processes is to reduce the universe of algorithm components available to the modeling process.
It has yet to be appreciated that the number of algorithm components available to an iterative modeling process can be dramatically reduced by determining which components are and are not significant to a solution.
Thus, there is still a need in the art for iterative feature selection methods as applied to iterative modeling processes.